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ghc/ghc

https://github.com/ghc/ghc.git · scanned 2026-05-16 13:30 UTC (1 day, 8 hours ago) · 10 languages

249 findings (9 legacy + 240 scanner) 8/10 scanners ran Scanner says 84 (lower by 30)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 day, 11 hours ago · v2 · 128 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

JSON
Combined
74.1
/100
Repobility
65
8 legacy
9-layer
84
100.0% cov · 120 gaps
Critical
1
click to filter
Agents
6
0 votes
Crowd
0
reported
{# ── 2026-05-17 R27 #5: score breakdown panel ────────────────────── Surfaces the score_breakdown JSON that's been silently stored on Repository for months. Turns hidden math into a trust signal. #}
Severity distribution — click a segment to filter
Active filters: source: crowd × excluding tests × Reset all
Severity: Critical 1 High 4 Medium 19 Low 76 Source: Legacy 8 9-layer 120 Crowd 0 Layer: Quality 54 Security 9 Software 40 Api 1 Frontend 22 Network 2
Scan summary Repository scanned at 83.5/100 with 100.0% coverage. It contains 4281 nodes across 0 cross-layer flows, written primarily in mixed languages. Engine surfaced 120 findings — concentrated in quality (49), software (38), frontend (22). Risk profile is high: 1 critical, 2 high, 16 medium. Recommended next step: open the quality layer findings first — that's where the highest-impact wins live.

Showing 0 of 128 findings. Click TP / FP to vote on a finding's accuracy — votes adjust the confidence weighting and improve detection across the platform.

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{# ── 2026-05-17 Round 14: AI-agent bridge footer ────────────────────── Discoverability: the /agents/voting/ guide + MCP manifest exist but aren't linked from anywhere users actually land. Small, opt-in footer. #}
For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.
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